U.S. patent number 11,209,263 [Application Number 16/712,228] was granted by the patent office on 2021-12-28 for 3d hand pose estimation based on depth-image guided adversarial network 1.
This patent grant is currently assigned to TENCENT AMERICA LLC. The grantee listed for this patent is TENCENT AMERICA LLC. Invention is credited to Wei Fan, Lianyi Han, Chao Huang, Shih-Yao Lin, Hui Tang, Yusheng Xie.
United States Patent |
11,209,263 |
Lin , et al. |
December 28, 2021 |
3D hand pose estimation based on depth-image guided adversarial
network 1
Abstract
A computer-implemented method, computer readable storage medium,
and computer system is provided for estimating three-dimensional
(3D) hand poses in images by receiving data corresponding to a hand
image, generating a depth map corresponding to the received hand
image data, and estimating a hand pose from the received hand image
data and the generated depth map.
Inventors: |
Lin; Shih-Yao (Palo Alto,
CA), Xie; Yusheng (Mountain View, CA), Tang; Hui
(Mountain View, CA), Huang; Chao (Palo Alto, CA), Han;
Lianyi (Palo Alto, CA), Fan; Wei (New York, NY) |
Applicant: |
Name |
City |
State |
Country |
Type |
TENCENT AMERICA LLC |
Palo Alto |
CA |
US |
|
|
Assignee: |
TENCENT AMERICA LLC (Palo Alto,
CA)
|
Family
ID: |
1000006020307 |
Appl.
No.: |
16/712,228 |
Filed: |
December 12, 2019 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20210180942 A1 |
Jun 17, 2021 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T
7/529 (20170101); G06T 7/74 (20170101); G06T
7/55 (20170101); G06N 3/08 (20130101); G06K
9/00355 (20130101); G01B 11/22 (20130101); G06T
2207/10028 (20130101); G06T 2200/04 (20130101) |
Current International
Class: |
G01B
11/22 (20060101); G06N 3/08 (20060101); G06T
7/73 (20170101); G06T 7/55 (20170101); G06K
9/00 (20060101); G06T 7/529 (20170101) |
Field of
Search: |
;382/103 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Real-Time Continuous Pose Recovery of Human Hands Using
Convolutional Networks ACM Transactions on Graphics vol. 33 Issue 5
Aug. 2014 Article No. 169pp. 1-10 https://doi.org/10.1145/2629500
(Year: 2014). cited by examiner .
Written Opinion in International Application No. PCT/US20/49855,
dated Dec. 1, 2020. cited by applicant .
International Search Report in International Application No.
PCT/US20/49855, dated Dec. 1, 2020. cited by applicant .
Chengde Wan et al, "Crossing Nets: Combining GANs and VAEs with a
Shared Latent Space for Hand Pose Estimation", 2017 IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), Nov. 9, 2017,
pp. 680-689 (11 pages Total). cited by applicant .
Mueller. F et al., "Real-time Hand Tracking under Occlusion from an
Egocentric RGB-D Sensor", 2017 IEEE International Conference on
Computer Vision (ICCV), Dec. 25, 2017, (11 Pages Total). cited by
applicant .
Wang. M et al., "DRPose3D: Depth Ranking in 3D Human Pose
Estimation" IJCAI-18: Proceedings of the 27th International Joint
Conference on Artificial Intelligence, Jul. 2018, pp. 978-984 (8
pages). cited by applicant .
He, W et al. "Synthesizing Depth Hand Images with GANs and Style
Transfer for Hand Pose Estimation", MDPI, sensors, Jul. 1, 2019,
(30 Pages Total). cited by applicant.
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Primary Examiner: Saini; Amandeep
Attorney, Agent or Firm: Sughrue Mion, PLLC
Claims
What is claimed is:
1. A method of estimating three-dimensional (3D) hand poses in
images, comprising: receiving, by a computer, data corresponding to
a hand image; generating, by the computer, a first depth map
corresponding to the received hand image data; generating, by the
computer, one or more hand joint heat maps based on the received
hand image data; estimating, by the computer, joint locations based
on the generated one or more hand joint heat maps; reconstructing,
by the computer, a second depth map based on the estimated joint
locations and the first depth map; and estimating, by the computer,
a hand pose from the received hand image data and the first and
second depth maps.
2. The method of claim 1, wherein the generating the first depth
map is performed by a generative adversarial network.
3. The method of claim 2, wherein the generative adversarial
network comprises a generator and a discriminator.
4. The method of claim 3, wherein the generative adversarial
network utilizes a generative adversarial network (GAN) loss value
and a task loss value as training values.
5. The method of claim 4, wherein the generative adversarial
network is trained by the generator minimizing the GAN loss value
and the task loss value.
6. The method of claim 4, wherein the generative adversarial
network is trained by the discriminator maximizing the GAN loss
value and the task loss value.
7. The method of claim 4, wherein the generator generates the first
depth map using solely the received hand image data.
8. The method of claim 7, wherein the discriminator determines the
GAN loss value using the first depth map and one or more unpaired
depth maps.
9. The method of claim 1, wherein the hand pose is estimated by
using only the received hand image data and the first and second
depth maps corresponding to the received hand image data.
10. A computer system for estimating three-dimensional (3D) hand
poses in images, the computer system comprising: one or more
computer-readable non-transitory storage media configured to store
computer program code; and one or more computer processors
configured to access said computer program code and operate as
instructed by said computer program code, said computer program
code including: receiving code configured to cause the one or more
computer processors to receive a data corresponding to a hand
image; first generating code configured to cause the one or more
computer processors to generate a first depth map corresponding to
the received hand image data; second generating code configured to
cause the one or more computer processors to generate one or more
hand joint heat maps based on the received hand image data; first
estimating code configured to cause the one or more computer
processors to estimate joint locations based on the generated one
or more hand joint heat maps; and reconstructing code configured to
cause the one or more computer processors to reconstruct a second
depth map based on the estimated joint locations and the first
depth map; and second estimating code configured to cause the one
or more computer processors to estimate a hand pose from the
received hand image data and the first and second depth maps.
11. The computer system of claim 10, wherein the generating is
performed by a generative adversarial network.
12. The computer system of claim 11, wherein the generative
adversarial network comprises a generator and a discriminator.
13. The computer system of claim 12, wherein the generative
adversarial network utilizes a generative adversarial network (GAN)
loss value and a task loss value as training values.
14. The computer system of claim 13, wherein the generative
adversarial network is trained by the generator minimizing the GAN
loss value and the task loss value.
15. The computer system of claim 13, wherein the generative
adversarial network is trained by the discriminator maximizing the
GAN loss value and the task loss value.
16. The computer system of claim 13, wherein the generator
generates the first depth map using solely the received hand image
data.
17. The computer system of claim 16, wherein the discriminator
determines the GAN loss value using the first depth map and one or
more unpaired depth maps.
18. A non-transitory computer readable medium having stored thereon
a computer program for estimating three-dimensional (3D) hand poses
in images, the computer program configured to cause one or more
computer processors to: receive a hand image data; generate a first
depth map corresponding to the received hand image data; generate
one or more hand joint heat maps based on the received hand image
data; estimate joint locations based on the one or more hand joint
heat maps; reconstruct a second depth map based on the estimated
joint locations and the first depth map; and estimate a hand pose
from the received hand image data and the first and second depth
maps.
19. The computer system of claim 13, wherein the hand pose is
estimated by using only the received hand image data and the first
and second depth maps corresponding to the received hand image
data.
20. The non-transitory computer readable medium of claim 18,
wherein the first depth map is generated by a generative
adversarial network.
Description
BACKGROUND
This disclosure relates generally to field of computing, and more
particularly to estimating 3D hand poses.
Hand pose estimation is the task of finding the joints of the hand
from an image or a set of video frames. Estimating
three-dimensional (3D) hand poses from red-green-blue (RGB) color
images is essential to a wide range of potential applications, such
as computer vision, virtual reality, augmented reality, and other
forms of human-computer interaction. Estimating hand poses from RGB
images has become significantly more popular due to the
accessibility of capturing RGB images through webcams, Internet of
Thing (IoT) cameras, and smartphones.
SUMMARY
Embodiments relate to a method, system, and computer readable
medium for estimating 3D hand poses. According to one aspect, a
method for estimating 3D hand poses is provided. The method may
include receiving, by a computer, data corresponding to a hand
image and generating a depth map corresponding to the received hand
image data. The computer may estimate a hand pose from the received
hand image data and the generated depth map.
According to another aspect, a computer system for estimating 3D
hand poses is provided. The computer system may include one or more
processors, one or more computer-readable memories, one or more
computer-readable tangible storage devices, and program
instructions stored on at least one of the one or more storage
devices for execution by at least one of the one or more processors
via at least one of the one or more memories, whereby the computer
system is capable of performing a method. The method may include
receiving, by a computer, data corresponding to a hand image and
generating a depth map corresponding to the received hand image
data. The computer may estimate a hand pose from the received hand
data image and the generated depth map.
According to yet another aspect, a computer readable medium for
estimating 3D hand poses is provided. The computer readable medium
may include one or more computer-readable storage devices and
program instructions stored on at least one of the one or more
tangible storage devices, the program instructions executable by a
processor. The program instructions are executable by a processor
for performing a method that may accordingly include receiving, by
a computer, a data corresponding to a hand image and generating a
depth map corresponding to the received hand image data. The
computer may estimate a hand pose from the received hand image data
and the generated depth map.
BRIEF DESCRIPTION OF THE DRAWINGS
These and other objects, features and advantages will become
apparent from the following detailed description of illustrative
embodiments, which is to be read in connection with the
accompanying drawings. The various features of the drawings are not
to scale as the illustrations are for clarity in facilitating one
skilled in the art in understanding this disclosure in conjunction
with the detailed description. In the drawings:
FIG. 1 illustrates a networked computer environment according to at
least one embodiment;
FIG. 2 is a functional block diagram of a program that estimates 3D
hand poses, according to at least one embodiment;
FIG. 3 is a functional block diagram of a depth map reconstruction
module as depicted in FIG. 2, according to at least one
embodiment;
FIG. 4 functional block diagram of a hand pose estimation module as
depicted in FIG. 2, according to at least one embodiment;
FIG. 5 is an operational flowchart illustrating the steps carried
out by a program that estimates 3D hand poses, according to at
least one embodiment;
FIG. 6 is a block diagram of an illustrative cloud computing
environment including the computer system depicted in FIG. 1,
according to at least one embodiment;
FIG. 7 is a block diagram of functional layers of the illustrative
cloud computing environment of FIG. 6, according to at least one
embodiment; and
FIG. 8 is a block diagram of internal and external components of
computers and servers depicted in FIG. 1 according to at least one
embodiment.
DETAILED DESCRIPTION
Detailed embodiments of the claimed structures and methods are
disclosed herein; however, it can be understood that the disclosed
embodiments are merely illustrative of the claimed structures and
methods that may be embodied in various forms. Aspects of this
disclosure may, however, be embodied in many different forms and
should not be construed as limited to the exemplary embodiments set
forth herein. Rather, these exemplary embodiments are provided so
that this disclosure will be thorough and complete and will fully
convey the scope of this disclosure to those skilled in the art. In
the description, details of well-known features and techniques may
be omitted to avoid unnecessarily obscuring the presented
embodiments.
Embodiments relate generally to the field of computing, and more
particularly to estimating 3D hand poses. The following described
exemplary embodiments provide a system, method and program product
to, among other things, determine 3D hand poses present within 2D
RGB images using only the 2D RGB images. Therefore, the
computer-implemented method, computer system, and computer readable
medium disclosed herein have the capacity to improve the field of
computing by allowing for computers to determine 3D shapes from 2D
images without the use of corresponding depth maps. Furthermore,
while the method, system, and computer readable medium disclosed
herein are described with respect to hand poses, the described
embodiments may also be configured for improved estimation of other
3D shapes.
As previously described, hand pose estimation is the task of
finding the joints of the hand from an image or a set of video
frames. Estimating three-dimensional (3D) hand poses from color RGB
images is essential to a wide range of potential applications, such
as computer vision, virtual reality, augmented reality, and other
forms of human-computer interaction. Estimating hand poses from RGB
images has become significantly more popular due to the
accessibility of capturing RGB images through webcams, Internet of
Thing (IoT) cameras, and smartphones.
However, estimating these poses is challenging due to ambiguity in
inferring depth information from RGB images. Hand pose estimators
may regularize 3D hand pose estimation models during training to
enforce the consistency between the predicted 3D poses and the
ground truth depth maps, but these estimators rely on the
availability of both RGB images and paired depth maps during
training. It may be advantageous, therefore, to utilize a
conditional generative adversarial network (GAN) model to generate
realistic depth maps conditioned on the input RGB image and to use
the synthesized depth maps to regularize the 3D hand pose
estimation model. Such a Depth-image Guided GAN (DGGAN) may
eliminate the need for depth maps to be uploaded simultaneously
with RGB images to be analyzed. A DGGAN may also effectively
regularize the pose estimation model.
Aspects are described herein with reference to flowchart
illustrations and/or block diagrams of methods, apparatus
(systems), and computer readable media according to certain
embodiments. It will be understood that each block of the flowchart
illustrations and/or block diagrams, and combinations of blocks in
the flowchart illustrations and/or block diagrams, can be
implemented by computer readable program instructions.
The following described exemplary embodiments provide a system,
method and program product that estimates 3D hand poses. According
to the present embodiment, this estimation may be performed by
utilizing a Depth-image Guided GAN on a monocular RGB image to
infer the depth-map from the RGB image.
Referring now to FIG. 1, a functional block diagram of a networked
computer environment illustrating a hand pose estimation system 100
(hereinafter "system") for improved estimation of 3D hand poses in
images is shown. It should be appreciated that FIG. 1 provides only
an illustration of one implementation and does not imply any
limitations with regard to the environments in which different
embodiments may be implemented. Many modifications to the depicted
environments may be made based on design and implementation
requirements.
The system 100 may include a computer 102 and a server computer
114. The computer 102 may communicate with the server computer 114
via a communication network 110 (hereinafter "network"). The
computer 102 may include a processor 104 and a software program 108
that is stored on a data storage device 106 and is enabled to
interface with a user and communicate with the server computer 114.
As will be discussed below with reference to FIG. 8 the computer
102 may include internal components 800A and external components
900A, respectively, and the server computer 114 may include
internal components 800B and external components 900B,
respectively. The computer 102 may be, for example, a mobile
device, a telephone, a personal digital assistant, a netbook, a
laptop computer, a tablet computer, a desktop computer, or any type
of computing devices capable of running a program, accessing a
network, and accessing a database.
The server computer 114 may also operate in a cloud computing
service model, such as Software as a Service (SaaS), Platform as a
Service (PaaS), or Infrastructure as a Service (IaaS), as discussed
below with respect to FIGS. 6 and 7. The server computer 114 may
also be located in a cloud computing deployment model, such as a
private cloud, community cloud, public cloud, or hybrid cloud.
The server computer 114, which may be used for estimating 3D hand
poses in images is enabled to run an Hand Pose Estimation Program
116 (hereinafter "program") that may interact with a database 112.
The Hand Pose Estimation Program method is explained in more detail
below with respect to FIG. 4. In one embodiment, the computer 102
may operate as an input device including a user interface while the
program 116 may run primarily on server computer 114. In an
alternative embodiment, the program 116 may run primarily on one or
more computers 102 while the server computer 114 may be used for
processing and storage of data used by the program 116. It should
be noted that the program 116 may be a standalone program or may be
integrated into a larger hand pose estimation program.
It should be noted, however, that processing for the program 116
may, in some instances be shared amongst the computers 102 and the
server computers 114 in any ratio. In another embodiment, the
program 116 may operate on more than one computer, server computer,
or some combination of computers and server computers, for example,
a plurality of computers 102 communicating across the network 110
with a single server computer 114. In another embodiment, for
example, the program 116 may operate on a plurality of server
computers 114 communicating across the network 110 with a plurality
of client computers. Alternatively, the program may operate on a
network server communicating across the network with a server and a
plurality of client computers.
The network 110 may include wired connections, wireless
connections, fiber optic connections, or some combination thereof.
In general, the network 110 can be any combination of connections
and protocols that will support communications between the computer
102 and the server computer 114. The network 110 may include
various types of networks, such as, for example, a local area
network (LAN), a wide area network (WAN) such as the Internet, a
telecommunication network such as the Public Switched Telephone
Network (PSTN), a wireless network, a public switched network, a
satellite network, a cellular network (e.g., a fifth generation
(5G) network, a long-term evolution (LTE) network, a third
generation (3G) network, a code division multiple access (CDMA)
network, etc.), a public land mobile network (PLMN), a metropolitan
area network (MAN), a private network, an ad hoc network, an
intranet, a fiber optic-based network, or the like, and/or a
combination of these or other types of networks.
The number and arrangement of devices and networks shown in FIG. 1
are provided as an example. In practice, there may be additional
devices and/or networks, fewer devices and/or networks, different
devices and/or networks, or differently arranged devices and/or
networks than those shown in FIG. 1. Furthermore, two or more
devices shown in FIG. 1 may be implemented within a single device,
or a single device shown in FIG. 1 may be implemented as multiple,
distributed devices. Additionally, or alternatively, a set of
devices (e.g., one or more devices) of system 100 may perform one
or more functions described as being performed by another set of
devices of system 100.
Referring to FIG. 2, a block diagram 200 of an Hand Pose Estimation
Program 116 (FIG. 1) is depicted. FIG. 2 may be described with the
aid of the exemplary embodiments depicted in FIG. 1. According to
one or more embodiments, the Hand Pose Estimation Program 116 may
be located on the computer 102 (FIG. 1) or on the server computer
114 (FIG. 1). The Hand Pose Estimation Program 116 may include,
among other things, a Depth Map Reconstruction Module 202 and a
Hand Pose Estimation Module 204. According to one embodiment, the
Hand Pose Estimation Program 116 may retrieve hand pose image data
206, corresponding to one or more hand pose images, from the
database 112 (FIG. 1) on the server computer 114. According to an
alternative embodiment, the hand pose image data 206 may be stored
in the data storage device 106 (FIG. 1) on the computer 102, and
the Hand Pose Estimation Program 116 may receive the hand pose
image data 206 from the computer 102 over the communication network
110 (FIG. 1). The Depth Map Reconstruction Module 202 may receive
the hand pose image data 206 as an input and may output one or more
generated depth maps 208 corresponding to the hand pose image data
206. The Depth Map Reconstruction Module 202 may additionally
calculate a generative adversarial network loss value L.sub.GAN.
The Hand Pose Estimation Module 204 may subsequently receive the
hand pose image data 206 and the generated depth maps 208 as inputs
and may output a determination of the 3D hand pose within the hand
pose image data 206. The Hand Pose Estimation Module 204 may
additionally calculate a task loss value L.sub.task.
Referring now to FIG. 3, a functional block diagram of a Depth Map
Reconstruction Module 202 (FIG. 2) is depicted. The Depth Map
Reconstruction Module 202 may be used, among other things, to
alleviate the requirement of a depth map paired to the RGB for
training. The Depth Map Reconstruction Module 202 may receive an
RGB hand image and may generate its underlying depth map
corresponding to the received RGB image. A set of unpaired training
depth images may be used in order to train the Depth Map
Reconstruction Module 202, so that an inferred depth map generated
by the Depth Map Reconstruction Module 202 may be similar to a real
depth map.
The Depth Map Reconstruction Module 202 may be constructed as an
generative adversarial network which may infer the depth map
corresponding to the input RGB image. Thus, the Depth Map
Reconstruction Module 202 may accordingly include a generator 302,
a discriminator 304, and data links 306, 308, 310, and 312. The
generator 302 may receive an RGB image input via the data link 306
and generate a depth map 208 (FIG. 2) based solely on the received
RGB image. The generator 302 may output the generated depth map 208
via the data link 308. The generator 302 may be trained based on
minimizing the L.sub.GAN and L.sub.task loss values. The
discriminator 304 may be configured to distinguish between a real
depth map and a generated depth map. The discriminator 304 may
receive the generated depth map 208 via the data link 308 and may
also receive one or more unpaired depth maps via the data link 310.
The discriminator 304 may determine, based on the unpaired depth
maps, the L.sub.GAN value corresponding to the generated depth map
208. The discriminator 304 may output the L.sub.GAN value via the
data link 312. The discriminator 304 may be trained based on
maximizing the L.sub.GAN and L.sub.task loss values.
Referring now to FIG. 4, a functional block diagram of a Hand Pose
Estimation Module 204 (FIG. 2) is depicted. The Hand Pose
Estimation Module 204 may, among other things, receive an RGB image
paired with an inferred (i.e., generated) depth map. The Hand Pose
Estimation Module 204 may accordingly include, a 2D Hand Pose
Estimator 402, a 3D Hand Pose Estimator 404, a Depth Regularizer
406, and data links 408, 410, 412, 414, 416, 418, 420, and 422. The
2D Hand Pose Estimator 402 may receive an RGB image as an input via
the data link 408 and may output a hand joint heat map via the data
link 410. The hand joint heat map may be sent to the 3D Hand Pose
Estimator 404 via the data link 410 in order to estimate one or
more 3D joint locations from the hand joint heat map. The estimated
3D joint locations may be output to the Depth Regularizer 406 via
the data link 412. The Depth Regularizer 406 may reconstructs a
depth map from the received 3D joint locations and may be trained
with the generated depth map 208 (FIG. 2), which may be received
via the data link 414. The reconstructed depth map may be output
via the data link 416. A two-dimensional loss value L.sub.2D, a
z-axis loss value L.sub.z, and a depth loss value L.sub.dep may be
output by the 2D Hand Pose Estimator 402, the 3D Hand Pose
Estimator 404, and the Depth Regularizer 406 via the data links
418, 420, and 422, respectively. These loss values may be used to
further train the hand pose estimation system.
Referring now to FIG. 5, an operational flowchart 500 illustrating
the steps carried out by a program that estimates 3D hand poses is
depicted. FIG. 5 may be described with the aid of FIGS. 1, 2, 3,
and 4. As previously described, the Hand Pose Estimation Program
116 (FIG. 1) may quickly and effectively estimate 3D hand poses
from RGB images.
At 502, data corresponding to a hand image is received by the
computer. The hand image data may be, for example, an RGB image
containing a 3D hand pose. It may be appreciated that the RGB may
be in substantially any raster or vector format, such as JPG, PNG,
GIF, TIFF, BMP, SVG or the like. In operation, the Hand Pose
Estimation Program 116 (FIG. 1) may reside on the computer 102
(FIG. 1) or on the server computer 114 (FIG. 1). The Hand Pose
Estimation Program 116 may receive the hand pose image data 206
(FIG. 2) over the communication network 110 (FIG. 1) or may
retrieve the hand pose image data 206 from the database 112 (FIG.
1).
At 504, a depth map corresponding to the received hand image data
is generated by the computer. The generated depth map may be
generated using only the received RGB image and may be used in
order to estimate the hand poses in an RGB image that does not
otherwise have a corresponding depth map. In operation, the
generator 302 (FIG. 3) of the Depth Map Reconstruction Module 202
(FIG. 2) may receive the hand pose image data 206 (FIG. 2) via the
data link 306 (FIG. 3). The generator 302 may output the generated
depth map 208 (FIG. 2) via the data link 308 (FIG. 3).
At 506, one or more hand joint heat maps are generated by the
computer, based on the received hand image data. The hand joint
heat maps may be used for 2D hand pose estimation in order to
determine the probable locations of the joints in the hand. In
operation, 2D Hand Pose Estimator 402 (FIG. 4) of the Hand Pose
Estimation Module 204 (FIG. 2) may receive the hand pose image data
206 (FIG. 2) via the data link 408. The Hand Pose Estimation Module
204 may generate one or more heat maps based on the received hand
pose image data 206 and may output the generated heat maps to the
3D Hand Pose Estimator 404 (FIG. 4) via the data link 410 (FIG.
4).
At 508, joint locations are estimated by the computer based on the
generated heat maps. Based on the probabilities in the heat maps,
the location of the joints in the hand may be estimated. By
estimating the locations of the hand joint, the pose of the hand
may be determined. In operation, the 3D Hand Pose Estimator 404
(FIG. 4) of the Hand Pose Estimation Module 204 (FIG. 2) may
receive the generated heat maps from the 2D Hand Pose Estimator 402
(FIG. 4) via the data link 410. The 3D Hand Pose Estimator 404 may
predict probable locations for the joints present in the hand pose
image data 206 (FIG. 2) and may output the location data to the
Depth Regularizer 406 (FIG. 4) via the data link 412.
At 510, a second depth map is generated by the computer based on
the estimated joint locations and the generated depth map. This
second depth map may refine the generated depth map based on
probable hand joint locations and may be used to further train the
system to estimate 3D hand poses. In operation, the Depth
Regularizer 406 (FIG. 4) of the Hand Pose Estimation Module 204
(FIG. 2) may receive the location data from the 3D Hand Pose
Estimator 404 (FIG. 4) via the data link 412. The Depth Regularizer
406 may generate a refined depth map using the generated depth map
208 (FIG. 2) received via the data link 414 (FIG. 4). The Depth
Regularizer 406 may output the reconstructed depth map via the data
link 416 (FIG. 4).
At 512, a hand pose is estimated from the received hand image data
and the generated depth maps. As discussed above, the estimated
hand pose may be used in applications such as virtual reality,
augmented reality, computer vision, and other applications. In
operation, the Hand Pose Estimation Module 204 (FIG. 2) may output
an estimated hand pose based on the generated depth map 208 (FIG.
2), the second depth map generated by the Depth Regularizer 406
(FIG. 4), and the hand pose image data 206 (FIG. 2).
It may be appreciated that FIG. 5 provides only an illustration of
one implementation and does not imply any limitations with regard
to how different embodiments may be implemented. Many modifications
to the depicted environments may be made based on design and
implementation requirements. For example, as discussed above, in
addition to estimating 3D hand poses, the method, computer system,
and computer readable medium disclosed herein may be used for the
detection and estimation of other 3D shapes.
It is understood in advance that although this disclosure includes
a detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, some embodiments are capable of being
implemented in conjunction with any other type of computing
environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
Referring to FIG. 6, illustrative cloud computing environment 600
is depicted. As shown, cloud computing environment 600 comprises
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Cloud computing nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 600 to offer
infrastructure, platforms and/or software as services for which a
cloud consumer does not need to maintain resources on a local
computing device. It is understood that the types of computing
devices 54A-N shown in FIG. 6 are intended to be illustrative only
and that cloud computing nodes 10 and cloud computing environment
600 can communicate with any type of computerized device over any
type of network and/or network addressable connection (e.g., using
a web browser).
Referring to FIG. 7, a set of functional abstraction layers 700
provided by cloud computing environment 600 (FIG. 6) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 7 are intended to be illustrative only and
embodiments of the disclosure are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 82 provide cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may comprise application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and Hand Pose
Estimation 96. Hand Pose Estimation 96 may estimate 3D hand poses
in received RGB images.
FIG. 8 is a block diagram 1000 of internal and external components
of computers 102 and 114 depicted in FIG. 1, in accordance with an
illustrative embodiment. It should be appreciated that FIG. 8
provides only an illustration of one implementation and does not
imply any limitations with regard to the environments in which
different embodiments may be implemented. Many modifications to the
depicted environments may be made based on design and
implementation requirements.
Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may include
respective sets of internal components 800A,B and external
components 900A,B illustrated in FIG. 5. Each of the sets of
internal components 800 include one or more processors 820, one or
more computer-readable RAMs 822 and one or more computer-readable
ROMs 824 on one or more buses 826, one or more operating systems
828, and one or more computer-readable tangible storage devices
830.
Processor 820 is implemented in hardware, firmware, or a
combination of hardware and software. Processor 820 is a central
processing unit (CPU), a graphics processing unit (GPU), an
accelerated processing unit (APU), a microprocessor, a
microcontroller, a digital signal processor (DSP), a
field-programmable gate array (FPGA), an application-specific
integrated circuit (ASIC), or another type of processing component.
In some implementations, processor 820 includes one or more
processors capable of being programmed to perform a function. Bus
826 includes a component that permits communication among the
internal components 800A,B.
The one or more operating systems 828, the software program 108
(FIG. 1) and the Hand Pose Estimation Program 116 (FIG. 1) on
server computer 114 (FIG. 1) are stored on one or more of the
respective computer-readable tangible storage devices 830 for
execution by one or more of the respective processors 820 via one
or more of the respective RAMs 822 (which typically include cache
memory). In the embodiment illustrated in FIG. 8, each of the
computer-readable tangible storage devices 830 is a magnetic disk
storage device of an internal hard drive. Alternatively, each of
the computer-readable tangible storage devices 830 is a
semiconductor storage device such as ROM 824, EPROM, flash memory,
an optical disk, a magneto-optic disk, a solid state disk, a
compact disc (CD), a digital versatile disc (DVD), a floppy disk, a
cartridge, a magnetic tape, and/or another type of non-transitory
computer-readable tangible storage device that can store a computer
program and digital information.
Each set of internal components 800A,B also includes a R/W drive or
interface 832 to read from and write to one or more portable
computer-readable tangible storage devices 936 such as a CD-ROM,
DVD, memory stick, magnetic tape, magnetic disk, optical disk or
semiconductor storage device. A software program, such as the
software program 108 (FIG. 1) and the Hand Pose Estimation Program
116 (FIG. 1) can be stored on one or more of the respective
portable computer-readable tangible storage devices 936, read via
the respective R/W drive or interface 832 and loaded into the
respective hard drive 830.
Each set of internal components 800A,B also includes network
adapters or interfaces 836 such as a TCP/IP adapter cards; wireless
Wi-Fi interface cards; or 3G, 4G, or 5G wireless interface cards or
other wired or wireless communication links. The software program
108 (FIG. 1) and the Hand Pose Estimation Program 116 (FIG. 1) on
the server computer 114 (FIG. 1) can be downloaded to the computer
102 (FIG. 1) and the server computer 114 from an external computer
via a network (for example, the Internet, a local area network or
other, wide area network) and respective network adapters or
interfaces 836. From the network adapters or interfaces 836, the
software program 108 and the Hand Pose Estimation Program 116 on
the server computer 114 are loaded into the respective hard drive
830. The network may comprise copper wires, optical fibers,
wireless transmission, routers, firewalls, switches, gateway
computers and/or edge servers.
Each of the sets of external components 900A,B can include a
computer display monitor 920, a keyboard 930, and a computer mouse
934. External components 900A,B can also include touch screens,
virtual keyboards, touch pads, pointing devices, and other human
interface devices. Each of the sets of internal components 800A,B
also includes device drivers 840 to interface to computer display
monitor 920, keyboard 930 and computer mouse 934. The device
drivers 840, R/W drive or interface 832 and network adapter or
interface 836 comprise hardware and software (stored in storage
device 830 and/or ROM 824).
Some embodiments may relate to a system, a method, and/or a
computer readable medium at any possible technical detail level of
integration. The computer readable medium may include a
computer-readable non-transitory storage medium (or media) having
computer readable program instructions thereon for causing a
processor to carry out operations.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program code/instructions for carrying out
operations may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, configuration data for integrated
circuitry, or either source code or object code written in any
combination of one or more programming languages, including an
object oriented programming language such as Smalltalk, C++, or the
like, and procedural programming languages, such as the "C"
programming language or similar programming languages. The computer
readable program instructions may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider). In some embodiments,
electronic circuitry including, for example, programmable logic
circuitry, field-programmable gate arrays (FPGA), or programmable
logic arrays (PLA) may execute the computer readable program
instructions by utilizing state information of the computer
readable program instructions to personalize the electronic
circuitry, in order to perform aspects or operations.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods, and computer readable media
according to various embodiments. In this regard, each block in the
flowchart or block diagrams may represent a module, segment, or
portion of instructions, which comprises one or more executable
instructions for implementing the specified logical function(s).
The method, computer system, and computer readable medium may
include additional blocks, fewer blocks, different blocks, or
differently arranged blocks than those depicted in the Figures. In
some alternative implementations, the functions noted in the blocks
may occur out of the order noted in the Figures. For example, two
blocks shown in succession may, in fact, be executed concurrently
or substantially concurrently, or the blocks may sometimes be
executed in the reverse order, depending upon the functionality
involved. It will also be noted that each block of the block
diagrams and/or flowchart illustration, and combinations of blocks
in the block diagrams and/or flowchart illustration, can be
implemented by special purpose hardware-based systems that perform
the specified functions or acts or carry out combinations of
special purpose hardware and computer instructions.
It will be apparent that systems and/or methods, described herein,
may be implemented in different forms of hardware, firmware, or a
combination of hardware and software. The actual specialized
control hardware or software code used to implement these systems
and/or methods is not limiting of the implementations. Thus, the
operation and behavior of the systems and/or methods were described
herein without reference to specific software code--it being
understood that software and hardware may be designed to implement
the systems and/or methods based on the description herein.
No element, act, or instruction used herein should be construed as
critical or essential unless explicitly described as such. Also, as
used herein, the articles "a" and "an" are intended to include one
or more items, and may be used interchangeably with "one or more."
Furthermore, as used herein, the term "set" is intended to include
one or more items (e.g., related items, unrelated items, a
combination of related and unrelated items, etc.), and may be used
interchangeably with "one or more." Where only one item is
intended, the term "one" or similar language is used. Also, as used
herein, the terms "has," "have," "having," or the like are intended
to be open-ended terms. Further, the phrase "based on" is intended
to mean "based, at least in part, on" unless explicitly stated
otherwise.
The descriptions of the various aspects and embodiments have been
presented for purposes of illustration, but are not intended to be
exhaustive or limited to the embodiments disclosed. Even though
combinations of features are recited in the claims and/or disclosed
in the specification, these combinations are not intended to limit
the disclosure of possible implementations. In fact, many of these
features may be combined in ways not specifically recited in the
claims and/or disclosed in the specification. Although each
dependent claim listed below may directly depend on only one claim,
the disclosure of possible implementations includes each dependent
claim in combination with every other claim in the claim set. Many
modifications and variations will be apparent to those of ordinary
skill in the art without departing from the scope of the described
embodiments. The terminology used herein was chosen to best explain
the principles of the embodiments, the practical application or
technical improvement over technologies found in the marketplace,
or to enable others of ordinary skill in the art to understand the
embodiments disclosed herein.
* * * * *
References